Clinical Assistant Professor
Hematology-Oncology
Bone and Soft Tissue Sarcoma
Ewing Sarcoma
Osteogenic Sarcoma
Rhabdomyosarcoma
Wilms Tumor
Oregon Health and Sciences University Registrar, Portland, OR, 6/6/2011
Stanford Health Care at Lucile Packard Children's Hospital, Palo Alto, CA, 6/30/2014
University of Tennessee Pediatric Hematology and Oncology Fellowship, Memphis, TN, 6/30/2017
Pediatrics, American Board of Pediatrics, 2014
Pediatric Hematology-Oncology, American Board of Pediatrics, 2019
English
Spanish
OUTCOMES OF PEDIATRIC AND ADOLESCENT PATIENTS WITH METASTATIC SARCOMA TREATED WITH SURGICAL RESECTION OR STEREOTACTIC ABLATIVE RADIATION THERAPY ELSEVIER IRELAND LTD. 2023: S98
View details for Web of Science ID 001133676400225
AI Transformers for Radiation Dose Reduction in Serial Whole-Body PET Scans. Radiology. Artificial intelligence 2023; 5 (3): e220246
To develop a deep learning approach that enables ultra-low-dose, 1% of the standard clinical dosage (3 MBq/kg), ultrafast whole-body PET reconstruction in cancer imaging.In this Health Insurance Portability and Accountability Act-compliant study, serial fluorine 18-labeled fluorodeoxyglucose PET/MRI scans of pediatric patients with lymphoma were retrospectively collected from two cross-continental medical centers between July 2015 and March 2020. Global similarity between baseline and follow-up scans was used to develop Masked-LMCTrans, a longitudinal multimodality coattentional convolutional neural network (CNN) transformer that provides interaction and joint reasoning between serial PET/MRI scans from the same patient. Image quality of the reconstructed ultra-low-dose PET was evaluated in comparison with a simulated standard 1% PET image. The performance of Masked-LMCTrans was compared with that of CNNs with pure convolution operations (classic U-Net family), and the effect of different CNN encoders on feature representation was assessed. Statistical differences in the structural similarity index measure (SSIM), peak signal-to-noise ratio (PSNR), and visual information fidelity (VIF) were assessed by two-sample testing with the Wilcoxon signed rank t test.The study included 21 patients (mean age, 15 years ± 7 [SD]; 12 female) in the primary cohort and 10 patients (mean age, 13 years ± 4; six female) in the external test cohort. Masked-LMCTrans-reconstructed follow-up PET images demonstrated significantly less noise and more detailed structure compared with simulated 1% extremely ultra-low-dose PET images. SSIM, PSNR, and VIF were significantly higher for Masked-LMCTrans-reconstructed PET (P < .001), with improvements of 15.8%, 23.4%, and 186%, respectively.Masked-LMCTrans achieved high image quality reconstruction of 1% low-dose whole-body PET images.Keywords: Pediatrics, PET, Convolutional Neural Network (CNN), Dose Reduction Supplemental material is available for this article. © RSNA, 2023.
View details for DOI 10.1148/ryai.220246
View details for PubMedID 37293349
View details for PubMedCentralID PMC10245181
Improved Detection of Bone Metastases in Children and Young Adults with Ferumoxytol-enhanced MRI. Radiology. Imaging cancer 2023; 5 (2): e220080
Purpose To evaluate if ferumoxytol can improve the detection of bone marrow metastases at diffusion-weighted (DW) MRI in pediatric and young adult patients with cancer. Materials and Methods In this secondary analysis of a prospective institutional review board-approved study (ClinicalTrials.gov identifier NCT01542879), 26 children and young adults (age range: 2-25 years; 18 males) underwent unenhanced or ferumoxytol-enhanced whole-body DW MRI between 2015 and 2020. Two reviewers determined the presence of bone marrow metastases using a Likert scale. One additional reviewer measured signal-to-noise ratios (SNRs) and tumor-to-bone marrow contrast. Fluorine 18 (18F) fluorodeoxyglucose (FDG) PET and follow-up chest CT, abdominal and pelvic CT, and standard (non-ferumoxytol enhanced) MRI served as the reference standard. Results of different experimental groups were compared using generalized estimation equations, Wilcoxon rank sum test, and Wilcoxon signed rank test. Results The SNR of normal bone marrow was significantly lower at ferumoxytol-enhanced MRI compared with unenhanced MRI at baseline (21.380 ± 19.878 vs 102.621 ± 94.346, respectively; P = .03) and after chemotherapy (20.026 ± 7.664 vs 54.110 ± 48.022, respectively; P = .006). This led to an increased tumor-to-marrow contrast on ferumoxytol-enhanced MRI scans compared with unenhanced MRI scans at baseline (1397.474 ± 938.576 vs 665.364 ± 440.576, respectively; P = .07) and after chemotherapy (1099.205 ± 864.604 vs 500.758 ± 439.975, respectively; P = .007). Accordingly, the sensitivity and diagnostic accuracy for detecting bone marrow metastases were 96% (94 of 98) and 99% (293 of 297), respectively, with the use of ferumoxytol-enhanced MRI compared with 83% (106 of 127) and 95% (369 of 390) with the use of unenhanced MRI. Conclusion Use of ferumoxytol helped improve the detection of bone marrow metastases in children and young adults with cancer. Keywords: Pediatrics, Molecular Imaging-Cancer, Molecular Imaging-Nanoparticles, MR-Diffusion Weighted Imaging, MR Imaging, Skeletal-Appendicular, Skeletal-Axial, Bone Marrow, Comparative Studies, Cancer Imaging, Ferumoxytol, USPIO © RSNA, 2023 ClinicalTrials.gov registration no. NCT01542879 See also the commentary by Holter-Chakrabarty and Glover in this issue.
View details for DOI 10.1148/rycan.220080
View details for PubMedID 36999999
Low-count whole-body PET/MRI restoration: an evaluation of dose reduction spectrum and five state-of-the-art artificial intelligence models. European journal of nuclear medicine and molecular imaging 2023
To provide a holistic and complete comparison of the five most advanced AI models in the augmentation of low-dose 18F-FDG PET data over the entire dose reduction spectrum.In this multicenter study, five AI models were investigated for restoring low-count whole-body PET/MRI, covering convolutional benchmarks - U-Net, enhanced deep super-resolution network (EDSR), generative adversarial network (GAN) - and the most cutting-edge image reconstruction transformer models in computer vision to date - Swin transformer image restoration network (SwinIR) and EDSR-ViT (vision transformer). The models were evaluated against six groups of count levels representing the simulated 75%, 50%, 25%, 12.5%, 6.25%, and 1% (extremely ultra-low-count) of the clinical standard 3 MBq/kg 18F-FDG dose. The comparisons were performed upon two independent cohorts - (1) a primary cohort from Stanford University and (2) a cross-continental external validation cohort from Tübingen University - in order to ensure the findings are generalizable. A total of 476 original count and simulated low-count whole-body PET/MRI scans were incorporated into this analysis.For low-count PET restoration on the primary cohort, the mean structural similarity index (SSIM) scores for dose 6.25% were 0.898 (95% CI, 0.887-0.910) for EDSR, 0.893 (0.881-0.905) for EDSR-ViT, 0.873 (0.859-0.887) for GAN, 0.885 (0.873-0.898) for U-Net, and 0.910 (0.900-0.920) for SwinIR. In continuation, SwinIR and U-Net's performances were also discreetly evaluated at each simulated radiotracer dose levels. Using the primary Stanford cohort, the mean diagnostic image quality (DIQ; 5-point Likert scale) scores of SwinIR restoration were 5 (SD, 0) for dose 75%, 4.50 (0.535) for dose 50%, 3.75 (0.463) for dose 25%, 3.25 (0.463) for dose 12.5%, 4 (0.926) for dose 6.25%, and 2.5 (0.534) for dose 1%.Compared to low-count PET images, with near-to or nondiagnostic images at higher dose reduction levels (up to 6.25%), both SwinIR and U-Net significantly improve the diagnostic quality of PET images. A radiotracer dose reduction to 1% of the current clinical standard radiotracer dose is out of scope for current AI techniques.
View details for DOI 10.1007/s00259-022-06097-w
View details for PubMedID 36633614
Outcomes of Pediatric and Adolescent Patients with Metastatic Sarcoma Treated with Surgical Resection or Stereotactic Ablative Radiation Therapy (SABR) LIPPINCOTT WILLIAMS & WILKINS. 2022: S42
View details for Web of Science ID 000847787800089
Combination of ribociclib and gemcitabine for the treatment of medulloblastoma. Molecular cancer therapeutics 2022
Group3 (G3) medulloblastoma (MB) is one of the deadliest forms of the disease for which novel treatment is desperately needed. Here we evaluate ribociclib, a highly selective CDK4/6 inhibitor, with gemcitabine in mouse and human G3MBs. Ribociclib central nervous system (CNS) penetration was assessed by in vivo microdialysis and by immunohistochemistry and gene expression studies and found to be CNS-penetrant. Tumors from mice treated with short term oral ribociclib displayed inhibited RB phosphorylation, downregulated E2F target genes, and decreased proliferation. Survival studies to determine the efficacy of ribociclib and gemcitabine combination were performed on mice intracranially implanted with luciferase labelled mouse and human G3MBs. Treatment of mice with the combination of ribociclib and gemcitabine was well tolerated, slowed tumor progression and metastatic spread, and increased survival. Expression-based gene activity and cell state analysis investigated the effects of the combination after short and long-term treatments. Molecular analysis of treated versus untreated tumors showed a significant decrease in the activity and expression of genes involved in cell cycle progression and DNA damage response, and an increase in the activity and expression of genes implicated in neuronal identity and neuronal differentiation. Our findings in both mouse and human patient-derived orthotopic xenograft models, suggest that ribociclib and gemcitabine combination therapy warrants further investigation as a treatment strategy for children with G3MB.
View details for DOI 10.1158/1535-7163.MCT-21-0598
View details for PubMedID 35709750
USING GENETICALLY ENGINEERED MOUSE MODELS AND PATIENT-DERIVED ORTHOTOPIC XENOGRAFTS TO DEVELOP NEW THERAPIES FOR PEDIATRIC BRAIN TUMORS OXFORD UNIV PRESS INC. 2022: 188
View details for Web of Science ID 000840122400709
Validation of Deep Learning-based Augmentation for Reduced 18F-FDG Dose for PET/MRI in Children and Young Adults with Lymphoma. Radiology. Artificial intelligence 2021; 3 (6): e200232
Purpose: To investigate if a deep learning convolutional neural network (CNN) could enable low-dose fluorine 18 (18F) fluorodeoxyglucose (FDG) PET/MRI for correct treatment response assessment of children and young adults with lymphoma.Materials and Methods: In this secondary analysis of prospectively collected data (ClinicalTrials.gov identifier: NCT01542879), 20 patients with lymphoma (mean age, 16.4 years ± 6.4 [standard deviation]) underwent 18F-FDG PET/MRI between July 2015 and August 2019 at baseline and after induction chemotherapy. Full-dose 18F-FDG PET data (3 MBq/kg) were simulated to lower 18F-FDG doses based on the percentage of coincidence events (representing simulated 75%, 50%, 25%, 12.5%, and 6.25% 18F-FDG dose [hereafter referred to as 75%Sim, 50%Sim, 25%Sim, 12.5%Sim, and 6.25%Sim, respectively]). A U.S. Food and Drug Administration-approved CNN was used to augment input simulated low-dose scans to full-dose scans. For each follow-up scan after induction chemotherapy, the standardized uptake value (SUV) response score was calculated as the maximum SUV (SUVmax) of the tumor normalized to the mean liver SUV; tumor response was classified as adequate or inadequate. Sensitivity and specificity in the detection of correct response status were computed using full-dose PET as the reference standard.Results: With decreasing simulated radiotracer doses, tumor SUVmax increased. A dose below 75%Sim of the full dose led to erroneous upstaging of adequate responders to inadequate responders (43% [six of 14 patients] for 75%Sim; 93% [13 of 14 patients] for 50%Sim; and 100% [14 of 14 patients] below 50%Sim; P < .05 for all). CNN-enhanced low-dose PET/MRI scans at 75%Sim and 50%Sim enabled correct response assessments for all patients. Use of the CNN augmentation for assessing adequate and inadequate responses resulted in identical sensitivities (100%) and specificities (100%) between the assessment of 100% full-dose PET, augmented 75%Sim, and augmented 50%Sim images.Conclusion: CNN enhancement of PET/MRI scans may enable 50% 18F-FDG dose reduction with correct treatment response assessment of children and young adults with lymphoma.Keywords: Pediatrics, PET/MRI, Computer Applications Detection/Diagnosis, Lymphoma, Tumor Response, Whole-Body Imaging, Technology AssessmentClinical trial registration no: NCT01542879 Supplemental material is available for this article. ©RSNA, 2021.
View details for DOI 10.1148/ryai.2021200232
View details for PubMedID 34870211
A comprehensive circulating tumor DNA assay for detection of translocation and copy number changes in pediatric sarcomas. Molecular cancer therapeutics 2021
Most circulating tumor DNA (ctDNA) assays are designed to detect recurrent mutations. Pediatric sarcomas share few recurrent mutations but rather are characterized by translocations and copy number changes. We applied CAncer Personalized Profiling by deep Sequencing (CAPP-Seq) for detection of translocations found in the most common pediatric sarcomas. We also applied ichorCNA to the combined off-target reads from our hybrid capture to simultaneously detect copy number alterations. We analyzed 64 prospectively collected plasma samples from 17 pediatric sarcoma patients. Translocations were detected in the pre-treatment plasma of 13 patients and were confirmed by tumor sequencing in 12 patients. Two of these patients had evidence of complex chromosomal rearrangements in their ctDNA. We also detected copy number changes in the pre-treatment plasma of 7 patients. We found that ctDNA levels correlated with metastatic status and clinical response. Furthermore, we detected rising ctDNA levels before relapse was clinically apparent, demonstrating the high sensitivity of our assay. This assay can be utilized for simultaneous detection of translocations and copy number alterations in the plasma of pediatric sarcoma patients. While we describe our experience in pediatric sarcomas, this approach can be applied to other tumors that are driven by structural variants.
View details for DOI 10.1158/1535-7163.MCT-20-0987
View details for PubMedID 34353895
Artificial intelligence enables whole-body positron emission tomography scans with minimal radiation exposure. European journal of nuclear medicine and molecular imaging 2021
PURPOSE: To generate diagnostic 18F-FDG PET images of pediatric cancer patients from ultra-low-dose 18F-FDG PET input images, using a novel artificial intelligence (AI) algorithm.METHODS: We used whole-body 18F-FDG-PET/MRI scans of 33 children and young adults with lymphoma (3-30years) to develop a convolutional neural network (CNN), which combines inputs from simulated 6.25% ultra-low-dose 18F-FDG PET scans and simultaneously acquired MRI scans to produce a standard-dose 18F-FDG PET scan. The image quality of ultra-low-dose PET scans, AI-augmented PET scans, and clinical standard PET scans was evaluated by traditional metrics in computer vision and by expert radiologists and nuclear medicine physicians, using Wilcoxon signed-rank tests and weighted kappa statistics.RESULTS: The peak signal-to-noise ratio and structural similarity index were significantly higher, and the normalized root-mean-square error was significantly lower on the AI-reconstructed PET images compared to simulated 6.25% dose images (p<0.001). Compared to the ground-truth standard-dose PET, SUVmax values of tumors and reference tissues were significantly higher on the simulated 6.25% ultra-low-dose PET scans as a result of image noise. After the CNN augmentation, the SUVmax values were recovered to values similar to the standard-dose PET. Quantitative measures of the readers' diagnostic confidence demonstrated significantly higher agreement between standard clinical scans and AI-reconstructed PET scans (kappa=0.942) than 6.25% dose scans (kappa=0.650).CONCLUSIONS: Our CNN model could generate simulated clinical standard 18F-FDG PET images from ultra-low-dose inputs, while maintaining clinically relevant information in terms of diagnostic accuracy and quantitative SUV measurements.
View details for DOI 10.1007/s00259-021-05197-3
View details for PubMedID 33527176
Connect with us:
Download our App: